Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations8950
Missing cells314
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory173.5 B

Variable types

Numeric17

Alerts

AVANCE_EFECTIVO is highly overall correlated with COMPRAS and 11 other fieldsHigh correlation
COMPRAS is highly overall correlated with AVANCE_EFECTIVO and 7 other fieldsHigh correlation
COMPRAS_PLAZOS is highly overall correlated with AVANCE_EFECTIVO and 13 other fieldsHigh correlation
COMPRAS_PUNTUALES is highly overall correlated with AVANCE_EFECTIVO and 13 other fieldsHigh correlation
F_AVANCE_EFECTIVO is highly overall correlated with COMPRAS_PLAZOS and 5 other fieldsHigh correlation
F_COMPRAS is highly overall correlated with AVANCE_EFECTIVO and 10 other fieldsHigh correlation
F_COMPRAS_PLAZOS is highly overall correlated with COMPRAS_PLAZOS and 5 other fieldsHigh correlation
F_COMPRAS_PUNTUALES is highly overall correlated with AVANCE_EFECTIVO and 8 other fieldsHigh correlation
F_PAGOS_COMPLETOS is highly overall correlated with AVANCE_EFECTIVO and 10 other fieldsHigh correlation
F_SALDO is highly overall correlated with AVANCE_EFECTIVO and 9 other fieldsHigh correlation
LÍMITE_CREDITO is highly overall correlated with F_PAGOS_COMPLETOS and 2 other fieldsHigh correlation
MESES_CLIENTE is highly overall correlated with AVANCE_EFECTIVO and 7 other fieldsHigh correlation
PAGOS is highly overall correlated with AVANCE_EFECTIVO and 10 other fieldsHigh correlation
PAGOS_MINIMOS is highly overall correlated with AVANCE_EFECTIVO and 8 other fieldsHigh correlation
P_AVANCE_EFECTIVO is highly overall correlated with AVANCE_EFECTIVO and 4 other fieldsHigh correlation
P_COMPRAS is highly overall correlated with COMPRAS_PLAZOS and 6 other fieldsHigh correlation
SALDO is highly overall correlated with AVANCE_EFECTIVO and 7 other fieldsHigh correlation
PAGOS_MINIMOS has 313 (3.5%) missing values Missing
F_SALDO has unique values Unique
COMPRAS has unique values Unique
COMPRAS_PUNTUALES has unique values Unique
COMPRAS_PLAZOS has unique values Unique
AVANCE_EFECTIVO has unique values Unique
F_COMPRAS has unique values Unique
F_COMPRAS_PUNTUALES has unique values Unique
F_COMPRAS_PLAZOS has unique values Unique
F_AVANCE_EFECTIVO has unique values Unique
PAGOS has unique values Unique
F_PAGOS_COMPLETOS has unique values Unique

Reproduction

Analysis started2025-03-13 03:41:11.428670
Analysis finished2025-03-13 03:41:38.214237
Duration26.79 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

SALDO
Real number (ℝ)

High correlation 

Distinct8949
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9382.3879
Minimum0
Maximum19043.139
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:38.302914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2854.8674
Q14275.0369
median12007.719
Q313740.566
95-th percentile16580.242
Maximum19043.139
Range19043.139
Interquartile range (IQR)9465.529

Descriptive statistics

Standard deviation5118.1136
Coefficient of variation (CV)0.54550223
Kurtosis-1.7041396
Mean9382.3879
Median Absolute Deviation (MAD)4947.4812
Skewness-0.0098065752
Sum83972372
Variance26195086
MonotonicityNot monotonic
2025-03-12T22:41:38.414145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13048.17538 2
 
< 0.1%
5323.148883 1
 
< 0.1%
2894.440826 1
 
< 0.1%
12568.17469 1
 
< 0.1%
12531.4608 1
 
< 0.1%
13456.59149 1
 
< 0.1%
12711.09785 1
 
< 0.1%
13388.78627 1
 
< 0.1%
5413.777486 1
 
< 0.1%
13511.28823 1
 
< 0.1%
Other values (8939) 8939
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
885.0757034 1
< 0.1%
1008.382846 1
< 0.1%
1043.390879 1
< 0.1%
1072.89289 1
< 0.1%
1098.764703 1
< 0.1%
1108.161419 1
< 0.1%
1118.313734 1
< 0.1%
1125.764249 1
< 0.1%
1175.757312 1
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18569.42652 1
< 0.1%
18497.07354 1
< 0.1%
18434.97796 1
< 0.1%
18397.4101 1
< 0.1%
18384.028 1
< 0.1%
18339.7197 1
< 0.1%
18308.96883 1
< 0.1%
18252.25624 1
< 0.1%
18233.67155 1
< 0.1%

F_SALDO
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45920527
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:38.543720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12088099
Q10.18849012
median0.30664968
Q30.7560605
95-th percentile0.83542186
Maximum1
Range1
Interquartile range (IQR)0.56757038

Descriptive statistics

Standard deviation0.28299772
Coefficient of variation (CV)0.61627717
Kurtosis-1.7668724
Mean0.45920527
Median Absolute Deviation (MAD)0.21291151
Skewness0.11596109
Sum4109.8871
Variance0.080087712
MonotonicityNot monotonic
2025-03-12T22:41:38.662859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.250692707 1
 
< 0.1%
0.725720857 1
 
< 0.1%
0.846129553 1
 
< 0.1%
0.138971189 1
 
< 0.1%
0.815383263 1
 
< 0.1%
0.653521965 1
 
< 0.1%
0.79179445 1
 
< 0.1%
0.771520379 1
 
< 0.1%
0.523217439 1
 
< 0.1%
0.190372367 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.010427583 1
< 0.1%
0.014856227 1
< 0.1%
0.021585154 1
< 0.1%
0.02194965 1
< 0.1%
0.023505901 1
< 0.1%
0.025894801 1
< 0.1%
0.027843806 1
< 0.1%
0.030474901 1
< 0.1%
0.032863616 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.939610773 1
< 0.1%
0.936658899 1
< 0.1%
0.935383007 1
< 0.1%
0.932518174 1
< 0.1%
0.931677127 1
< 0.1%
0.923863815 1
< 0.1%
0.920708863 1
< 0.1%
0.920004014 1
< 0.1%
0.919001899 1
< 0.1%

COMPRAS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30525.873
Minimum0
Maximum49039.57
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:38.771554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6616.995
Q127106.652
median30934.582
Q339825.161
95-th percentile43316.606
Maximum49039.57
Range49039.57
Interquartile range (IQR)12718.509

Descriptive statistics

Standard deviation11041.411
Coefficient of variation (CV)0.36170663
Kurtosis0.21198459
Mean30525.873
Median Absolute Deviation (MAD)6943.5872
Skewness-0.98275394
Sum2.7320657 × 108
Variance1.2191276 × 108
MonotonicityNot monotonic
2025-03-12T22:41:38.895670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26344.0722 1
 
< 0.1%
41437.06432 1
 
< 0.1%
43779.4274 1
 
< 0.1%
28258.13329 1
 
< 0.1%
42817.61455 1
 
< 0.1%
41108.43375 1
 
< 0.1%
44493.57963 1
 
< 0.1%
41012.43728 1
 
< 0.1%
26343.59408 1
 
< 0.1%
29907.99005 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
802.5621109 1
< 0.1%
1030.085201 1
< 0.1%
1095.306246 1
< 0.1%
1856.714976 1
< 0.1%
1877.730395 1
< 0.1%
1970.558086 1
< 0.1%
1980.687874 1
< 0.1%
1995.799837 1
< 0.1%
2046.231017 1
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
48228.03931 1
< 0.1%
47404.00743 1
< 0.1%
47381.97436 1
< 0.1%
47359.68992 1
< 0.1%
47141.1223 1
< 0.1%
46918.90384 1
< 0.1%
46891.48848 1
< 0.1%
46801.62683 1
< 0.1%
46647.03585 1
< 0.1%

COMPRAS_PUNTUALES
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22913.666
Minimum0
Maximum40761.25
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:39.019139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4333.5648
Q16698.2525
median31011.033
Q334495.416
95-th percentile36929.564
Maximum40761.25
Range40761.25
Interquartile range (IQR)27797.164

Descriptive statistics

Standard deviation13573.919
Coefficient of variation (CV)0.59239404
Kurtosis-1.7263916
Mean22913.666
Median Absolute Deviation (MAD)4975.9527
Skewness-0.40878713
Sum2.0507731 × 108
Variance1.8425128 × 108
MonotonicityNot monotonic
2025-03-12T22:41:39.133459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38237.44252 1
 
< 0.1%
5362.933728 1
 
< 0.1%
5033.507182 1
 
< 0.1%
36005.104 1
 
< 0.1%
6096.311227 1
 
< 0.1%
7480.473162 1
 
< 0.1%
7388.158309 1
 
< 0.1%
3651.156999 1
 
< 0.1%
31353.8312 1
 
< 0.1%
35957.10776 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
354.6224778 1
< 0.1%
916.1213738 1
< 0.1%
969.8717684 1
< 0.1%
1158.864719 1
< 0.1%
1198.052851 1
< 0.1%
1225.548277 1
< 0.1%
1314.469823 1
< 0.1%
1426.650583 1
< 0.1%
1467.924848 1
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40494.4017 1
< 0.1%
40411.58254 1
< 0.1%
40378.13101 1
< 0.1%
40349.45022 1
< 0.1%
40298.4978 1
< 0.1%
40104.52639 1
< 0.1%
39967.37278 1
< 0.1%
39947.12194 1
< 0.1%
39942.58007 1
< 0.1%

COMPRAS_PLAZOS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11407.852
Minimum0
Maximum22500
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:39.263682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2311.397
Q13736.7202
median10832.141
Q319127.083
95-th percentile20486.675
Maximum22500
Range22500
Interquartile range (IQR)15390.363

Descriptive statistics

Standard deviation7087.103
Coefficient of variation (CV)0.62124781
Kurtosis-1.6290421
Mean11407.852
Median Absolute Deviation (MAD)7629.1438
Skewness0.024009418
Sum1.0210027 × 108
Variance50227029
MonotonicityNot monotonic
2025-03-12T22:41:39.374884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3727.113162 1
 
< 0.1%
20804.5293 1
 
< 0.1%
18471.90619 1
 
< 0.1%
4536.012866 1
 
< 0.1%
19267.79499 1
 
< 0.1%
19557.49091 1
 
< 0.1%
20686.76067 1
 
< 0.1%
18522.99067 1
 
< 0.1%
9010.635829 1
 
< 0.1%
4235.515719 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
291.4350444 1
< 0.1%
306.3587599 1
< 0.1%
420.8859033 1
< 0.1%
428.5092786 1
< 0.1%
486.4719017 1
< 0.1%
627.4056257 1
< 0.1%
628.6425918 1
< 0.1%
648.6134924 1
< 0.1%
702.8437687 1
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
22445.19219 1
< 0.1%
22295.53069 1
< 0.1%
22154.51792 1
< 0.1%
22117.11387 1
< 0.1%
22021.44059 1
< 0.1%
22013.27978 1
< 0.1%
22012.8644 1
< 0.1%
22005.36194 1
< 0.1%
21951.52724 1
< 0.1%

AVANCE_EFECTIVO
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22573.474
Minimum0
Maximum47137.212
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:39.528082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7012.1109
Q110944.431
median19465.792
Q336150.723
95-th percentile40572.083
Maximum47137.212
Range47137.212
Interquartile range (IQR)25206.291

Descriptive statistics

Standard deviation12594.296
Coefficient of variation (CV)0.55792457
Kurtosis-1.5988803
Mean22573.474
Median Absolute Deviation (MAD)10850.018
Skewness0.21537149
Sum2.0203259 × 108
Variance1.5861629 × 108
MonotonicityNot monotonic
2025-03-12T22:41:39.664890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15708.23968 1
 
< 0.1%
36506.31873 1
 
< 0.1%
39084.83617 1
 
< 0.1%
7058.118437 1
 
< 0.1%
39118.40863 1
 
< 0.1%
31439.06137 1
 
< 0.1%
39705.07662 1
 
< 0.1%
38603.70918 1
 
< 0.1%
4625.479787 1
 
< 0.1%
12973.24828 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
444.180016 1
< 0.1%
767.2506051 1
< 0.1%
1667.744275 1
< 0.1%
1851.092454 1
< 0.1%
1875.702282 1
< 0.1%
1928.362354 1
< 0.1%
2369.725445 1
< 0.1%
2487.948408 1
< 0.1%
2647.126977 1
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
46470.12694 1
< 0.1%
46394.29124 1
< 0.1%
45747.75649 1
< 0.1%
45628.08385 1
< 0.1%
45476.09724 1
< 0.1%
45219.3376 1
< 0.1%
45164.82939 1
< 0.1%
45146.60223 1
< 0.1%
45141.25977 1
< 0.1%

F_COMPRAS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55981002
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:39.793809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20331681
Q10.40727318
median0.50530452
Q30.7770837
95-th percentile0.86479798
Maximum1
Range1
Interquartile range (IQR)0.36981052

Descriptive statistics

Standard deviation0.21405474
Coefficient of variation (CV)0.38237033
Kurtosis-1.0985196
Mean0.55981002
Median Absolute Deviation (MAD)0.17765663
Skewness-0.027693024
Sum5010.2997
Variance0.045819432
MonotonicityNot monotonic
2025-03-12T22:41:39.943428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.496535855 1
 
< 0.1%
0.883631682 1
 
< 0.1%
0.77727304 1
 
< 0.1%
0.388050467 1
 
< 0.1%
0.812677656 1
 
< 0.1%
0.832239216 1
 
< 0.1%
0.749221444 1
 
< 0.1%
0.777377855 1
 
< 0.1%
0.109919235 1
 
< 0.1%
0.411019006 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.040958035 1
< 0.1%
0.046248735 1
< 0.1%
0.046857727 1
< 0.1%
0.04998949 1
< 0.1%
0.050787224 1
< 0.1%
0.05638137 1
< 0.1%
0.060709924 1
< 0.1%
0.064944785 1
< 0.1%
0.067918099 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.999194116 1
< 0.1%
0.998470526 1
< 0.1%
0.997711347 1
< 0.1%
0.988014567 1
< 0.1%
0.986604764 1
< 0.1%
0.977364949 1
< 0.1%
0.974017266 1
< 0.1%
0.973889019 1
< 0.1%
0.973881002 1
< 0.1%

F_COMPRAS_PUNTUALES
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47803224
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:40.063962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.153228
Q10.25085559
median0.53352102
Q30.64911958
95-th percentile0.80963312
Maximum1
Range1
Interquartile range (IQR)0.39826399

Descriptive statistics

Standard deviation0.22187326
Coefficient of variation (CV)0.46413869
Kurtosis-1.2660164
Mean0.47803224
Median Absolute Deviation (MAD)0.20131508
Skewness-0.073036402
Sum4278.3885
Variance0.049227742
MonotonicityNot monotonic
2025-03-12T22:41:40.186698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.464442158 1
 
< 0.1%
0.308493142 1
 
< 0.1%
0.237406009 1
 
< 0.1%
0.566880671 1
 
< 0.1%
0.194713638 1
 
< 0.1%
0.202100795 1
 
< 0.1%
0.22500664 1
 
< 0.1%
0.179431 1
 
< 0.1%
0.752303194 1
 
< 0.1%
0.573390431 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.031308334 1
< 0.1%
0.031661187 1
< 0.1%
0.050872919 1
< 0.1%
0.054947643 1
< 0.1%
0.057340594 1
< 0.1%
0.057911991 1
< 0.1%
0.05884068 1
< 0.1%
0.059907398 1
< 0.1%
0.061399317 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.998947367 1
< 0.1%
0.984298283 1
< 0.1%
0.9823838 1
< 0.1%
0.980166562 1
< 0.1%
0.978558397 1
< 0.1%
0.977949401 1
< 0.1%
0.970915648 1
< 0.1%
0.968876608 1
< 0.1%
0.96837024 1
< 0.1%

F_COMPRAS_PLAZOS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62391568
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:40.365394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17461492
Q10.51823052
median0.60760758
Q30.81492919
95-th percentile0.89275532
Maximum1
Range1
Interquartile range (IQR)0.29669867

Descriptive statistics

Standard deviation0.2082072
Coefficient of variation (CV)0.33371048
Kurtosis-0.028872187
Mean0.62391568
Median Absolute Deviation (MAD)0.13699256
Skewness-0.66151997
Sum5584.0454
Variance0.043350238
MonotonicityNot monotonic
2025-03-12T22:41:40.506979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.821610917 1
 
< 0.1%
0.580985209 1
 
< 0.1%
0.514425934 1
 
< 0.1%
0.848701303 1
 
< 0.1%
0.593445387 1
 
< 0.1%
0.521138327 1
 
< 0.1%
0.552134929 1
 
< 0.1%
0.549162396 1
 
< 0.1%
0.222073909 1
 
< 0.1%
0.878886786 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.01843335 1
< 0.1%
0.028159527 1
< 0.1%
0.032930782 1
< 0.1%
0.036814876 1
< 0.1%
0.044216755 1
< 0.1%
0.045430799 1
< 0.1%
0.051737891 1
< 0.1%
0.053180794 1
< 0.1%
0.054541241 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.999228986 1
< 0.1%
0.994951549 1
< 0.1%
0.988006445 1
< 0.1%
0.985836738 1
< 0.1%
0.982656168 1
< 0.1%
0.981889332 1
< 0.1%
0.979582744 1
< 0.1%
0.979260456 1
< 0.1%
0.97764373 1
< 0.1%

F_AVANCE_EFECTIVO
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73494611
Minimum0
Maximum1.5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:40.619055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13012927
Q10.23071552
median0.94765112
Q31.0291717
95-th percentile1.3199466
Maximum1.5
Range1.5
Interquartile range (IQR)0.79845615

Descriptive statistics

Standard deviation0.42256259
Coefficient of variation (CV)0.57495724
Kurtosis-1.486247
Mean0.73494611
Median Absolute Deviation (MAD)0.15087962
Skewness-0.35139224
Sum6577.7677
Variance0.17855914
MonotonicityNot monotonic
2025-03-12T22:41:40.729977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.194501644 1
 
< 0.1%
0.951120651 1
 
< 0.1%
0.961277649 1
 
< 0.1%
0.178319851 1
 
< 0.1%
1.004010144 1
 
< 0.1%
1.146789182 1
 
< 0.1%
1.04901538 1
 
< 0.1%
1.060168412 1
 
< 0.1%
1.357379426 1
 
< 0.1%
0.193118907 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.005993094 1
< 0.1%
0.011819193 1
< 0.1%
0.013748532 1
< 0.1%
0.015169869 1
< 0.1%
0.016549447 1
< 0.1%
0.019757976 1
< 0.1%
0.020842636 1
< 0.1%
0.021314779 1
< 0.1%
0.022455455 1
< 0.1%
ValueCountFrequency (%)
1.5 1
< 0.1%
1.49104669 1
< 0.1%
1.478030738 1
< 0.1%
1.469423469 1
< 0.1%
1.468218163 1
< 0.1%
1.463748383 1
< 0.1%
1.456969054 1
< 0.1%
1.456421979 1
< 0.1%
1.45468774 1
< 0.1%
1.454311881 1
< 0.1%

P_AVANCE_EFECTIVO
Real number (ℝ)

High correlation 

Distinct118
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.169162
Minimum0
Maximum123
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:40.852220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q165
median82
Q392
95-th percentile103
Maximum123
Range123
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.260935
Coefficient of variation (CV)0.29225653
Kurtosis0.17016868
Mean76.169162
Median Absolute Deviation (MAD)11
Skewness-0.95661627
Sum681714
Variance495.54921
MonotonicityNot monotonic
2025-03-12T22:41:40.979546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 260
 
2.9%
84 250
 
2.8%
87 243
 
2.7%
90 234
 
2.6%
88 234
 
2.6%
83 231
 
2.6%
86 230
 
2.6%
81 230
 
2.6%
82 228
 
2.5%
89 223
 
2.5%
Other values (108) 6587
73.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 2
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
12 8
0.1%
13 8
0.1%
14 10
0.1%
ValueCountFrequency (%)
123 1
 
< 0.1%
121 1
 
< 0.1%
120 4
 
< 0.1%
119 2
 
< 0.1%
118 1
 
< 0.1%
117 2
 
< 0.1%
116 8
0.1%
115 4
 
< 0.1%
114 5
0.1%
113 10
0.1%

P_COMPRAS
Real number (ℝ)

High correlation 

Distinct331
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.98145
Minimum0
Maximum358
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2025-03-12T22:41:41.441365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59
Q197
median176
Q3216
95-th percentile287
Maximum358
Range358
Interquartile range (IQR)119

Descriptive statistics

Standard deviation71.322003
Coefficient of variation (CV)0.43230316
Kurtosis-0.9073774
Mean164.98145
Median Absolute Deviation (MAD)57
Skewness0.078237319
Sum1476584
Variance5086.8281
MonotonicityNot monotonic
2025-03-12T22:41:41.560859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209 72
 
0.8%
192 67
 
0.7%
196 66
 
0.7%
206 66
 
0.7%
214 65
 
0.7%
205 65
 
0.7%
201 64
 
0.7%
213 64
 
0.7%
194 62
 
0.7%
202 62
 
0.7%
Other values (321) 8297
92.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
8 1
 
< 0.1%
10 2
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
17 4
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
ValueCountFrequency (%)
358 1
 
< 0.1%
351 1
 
< 0.1%
350 1
 
< 0.1%
349 2
< 0.1%
348 1
 
< 0.1%
347 1
 
< 0.1%
345 1
 
< 0.1%
342 3
< 0.1%
340 1
 
< 0.1%
338 4
< 0.1%

LÍMITE_CREDITO
Real number (ℝ)

High correlation 

Distinct8949
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14696.64
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:41.676277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile3904.0634
Q113487.727
median14942.545
Q316269.727
95-th percentile26139.173
Maximum30000
Range29950
Interquartile range (IQR)2781.9998

Descriptive statistics

Standard deviation5537.0747
Coefficient of variation (CV)0.37675785
Kurtosis0.94614967
Mean14696.64
Median Absolute Deviation (MAD)1381.6762
Skewness0.018925049
Sum1.3152023 × 108
Variance30659196
MonotonicityNot monotonic
2025-03-12T22:41:41.792598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16819.48004 1
 
< 0.1%
13489.83458 1
 
< 0.1%
14023.51913 1
 
< 0.1%
15672.74711 1
 
< 0.1%
15771.35508 1
 
< 0.1%
15205.50881 1
 
< 0.1%
16927.76517 1
 
< 0.1%
14721.74873 1
 
< 0.1%
27347.93593 1
 
< 0.1%
16362.29711 1
 
< 0.1%
Other values (8939) 8939
99.9%
ValueCountFrequency (%)
50 1
< 0.1%
133.919532 1
< 0.1%
504.2268287 1
< 0.1%
580.0112044 1
< 0.1%
646.9910694 1
< 0.1%
680.3749224 1
< 0.1%
732.8415517 1
< 0.1%
776.9563119 1
< 0.1%
796.6130275 1
< 0.1%
867.128434 1
< 0.1%
ValueCountFrequency (%)
30000 1
< 0.1%
29905.60826 1
< 0.1%
29818.70965 1
< 0.1%
29669.97528 1
< 0.1%
29645.60622 1
< 0.1%
29544.21431 1
< 0.1%
29502.22828 1
< 0.1%
29339.72126 1
< 0.1%
29315.78973 1
< 0.1%
29293.07003 1
< 0.1%

PAGOS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19824.347
Minimum0
Maximum50721.483
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:41.904187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6088.4929
Q19329.6031
median21367.621
Q325219.127
95-th percentile43827.557
Maximum50721.483
Range50721.483
Interquartile range (IQR)15889.524

Descriptive statistics

Standard deviation11069.268
Coefficient of variation (CV)0.55836735
Kurtosis-0.067413052
Mean19824.347
Median Absolute Deviation (MAD)7608.515
Skewness0.67538125
Sum1.7742791 × 108
Variance1.225287 × 108
MonotonicityNot monotonic
2025-03-12T22:41:42.024962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21337.02746 1
 
< 0.1%
9120.361516 1
 
< 0.1%
8036.844158 1
 
< 0.1%
24779.04808 1
 
< 0.1%
7076.890278 1
 
< 0.1%
6738.704191 1
 
< 0.1%
8419.502739 1
 
< 0.1%
9107.000329 1
 
< 0.1%
37994.25 1
 
< 0.1%
24692.94081 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1503.315614 1
< 0.1%
1604.581013 1
< 0.1%
1899.629874 1
< 0.1%
1909.85867 1
< 0.1%
1924.758371 1
< 0.1%
2232.888478 1
< 0.1%
2289.554519 1
< 0.1%
2694.995305 1
< 0.1%
2706.069721 1
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
50472.32637 1
< 0.1%
50380.26434 1
< 0.1%
49623.81552 1
< 0.1%
49223.49462 1
< 0.1%
48952.659 1
< 0.1%
48937.42629 1
< 0.1%
48811.44262 1
< 0.1%
48727.94939 1
< 0.1%
48562.3712 1
< 0.1%

PAGOS_MINIMOS
Real number (ℝ)

High correlation  Missing 

Distinct8637
Distinct (%)100.0%
Missing313
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean44222.978
Minimum1006.065
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:42.138679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1006.065
5-th percentile17045.676
Q131572.128
median48780.272
Q357761.176
95-th percentile64111.498
Maximum76406.208
Range75400.143
Interquartile range (IQR)26189.048

Descriptive statistics

Standard deviation15468.314
Coefficient of variation (CV)0.34978002
Kurtosis-1.0697059
Mean44222.978
Median Absolute Deviation (MAD)12457.93
Skewness-0.35549673
Sum3.8195386 × 108
Variance2.3926875 × 108
MonotonicityNot monotonic
2025-03-12T22:41:42.248224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39452.95812 1
 
< 0.1%
56255.79157 1
 
< 0.1%
40940.8495 1
 
< 0.1%
59390.27296 1
 
< 0.1%
61481.52889 1
 
< 0.1%
50366.75465 1
 
< 0.1%
33988.64039 1
 
< 0.1%
27619.23845 1
 
< 0.1%
59918.19036 1
 
< 0.1%
35039.86352 1
 
< 0.1%
Other values (8627) 8627
96.4%
(Missing) 313
 
3.5%
ValueCountFrequency (%)
1006.064965 1
< 0.1%
2160.629738 1
< 0.1%
3038.547949 1
< 0.1%
3210.910539 1
< 0.1%
4080.545925 1
< 0.1%
4400.615012 1
< 0.1%
4408.570208 1
< 0.1%
5081.015028 1
< 0.1%
5590.445073 1
< 0.1%
5776.494624 1
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
75375.06471 1
< 0.1%
73989.66709 1
< 0.1%
73852.0694 1
< 0.1%
73606.51716 1
< 0.1%
73136.23537 1
< 0.1%
72714.63958 1
< 0.1%
72650.9874 1
< 0.1%
72141.59346 1
< 0.1%
72065.2961 1
< 0.1%

F_PAGOS_COMPLETOS
Real number (ℝ)

High correlation  Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54541448
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:42.377792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13330137
Q10.33673232
median0.43268965
Q30.81517791
95-th percentile0.89000698
Maximum1
Range1
Interquartile range (IQR)0.47844559

Descriptive statistics

Standard deviation0.26744263
Coefficient of variation (CV)0.4903475
Kurtosis-1.5198974
Mean0.54541448
Median Absolute Deviation (MAD)0.28094339
Skewness-0.064546367
Sum4881.4596
Variance0.071525558
MonotonicityNot monotonic
2025-03-12T22:41:42.495914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.817906647 1
 
< 0.1%
0.414472591 1
 
< 0.1%
0.386041948 1
 
< 0.1%
0.823809481 1
 
< 0.1%
0.406787958 1
 
< 0.1%
0.356113009 1
 
< 0.1%
0.41343092 1
 
< 0.1%
0.326115516 1
 
< 0.1%
0.74219514 1
 
< 0.1%
0.909980664 1
 
< 0.1%
Other values (8940) 8940
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.012136856 1
< 0.1%
0.020148508 1
< 0.1%
0.021707718 1
< 0.1%
0.023528331 1
< 0.1%
0.029629837 1
< 0.1%
0.032417556 1
< 0.1%
0.032610948 1
< 0.1%
0.032906928 1
< 0.1%
0.033161956 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.993136775 1
< 0.1%
0.991642838 1
< 0.1%
0.991604933 1
< 0.1%
0.986776402 1
< 0.1%
0.983996727 1
< 0.1%
0.981609131 1
< 0.1%
0.980439591 1
< 0.1%
0.979336983 1
< 0.1%
0.978045132 1
< 0.1%

MESES_CLIENTE
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3706145
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size397.9 KiB
2025-03-12T22:41:42.584065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q17
median8
Q310
95-th percentile11
Maximum12
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7560353
Coefficient of variation (CV)0.2097857
Kurtosis-1.5628377
Mean8.3706145
Median Absolute Deviation (MAD)2
Skewness-0.13397164
Sum74917
Variance3.0836598
MonotonicityNot monotonic
2025-03-12T22:41:42.679514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 3336
37.3%
6 2091
23.4%
7 1414
15.8%
8 1021
 
11.4%
11 575
 
6.4%
9 512
 
5.7%
12 1
 
< 0.1%
ValueCountFrequency (%)
6 2091
23.4%
7 1414
15.8%
8 1021
 
11.4%
9 512
 
5.7%
10 3336
37.3%
11 575
 
6.4%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 575
 
6.4%
10 3336
37.3%
9 512
 
5.7%
8 1021
 
11.4%
7 1414
15.8%
6 2091
23.4%

Interactions

2025-03-12T22:41:36.363475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:12.156889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:14.081852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.484149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:16.891186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:18.576131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:19.968361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:21.384485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:22.951411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:24.352723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:25.742993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:27.446736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:28.883390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:30.322329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:31.760557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:33.166264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:34.942137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:36.449577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:12.275351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:14.168442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.572195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-03-12T22:41:13.709974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.155659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:16.556965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:18.215410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:19.639811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:21.054714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:22.619067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:24.017792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:25.413863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:27.102459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:28.550113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:29.980443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:31.419695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:32.831708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:34.591042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:36.029296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:37.526033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:13.803384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.235305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:16.636873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:18.302379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:19.717771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:21.132883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:22.697213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:24.097912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:25.494602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:27.188227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:28.630231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:30.063019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:31.501900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:32.909425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:34.676228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:36.109379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:37.616845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:13.912617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.323623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:16.727509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:18.398780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:19.801770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:21.221284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:22.785957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:24.190784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:25.583600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:27.281169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:28.719277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:30.154557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:31.592791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:32.999393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:34.767126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:36.200818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:37.698252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:13.992248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:15.403278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:16.808378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:18.485313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:19.885070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:21.301356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:22.865828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:24.269083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:25.661731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:27.362123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:28.800115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:30.235504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:31.675495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:33.080636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:34.854120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-12T22:41:36.280606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-12T22:41:42.756187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AVANCE_EFECTIVOCOMPRASCOMPRAS_PLAZOSCOMPRAS_PUNTUALESF_AVANCE_EFECTIVOF_COMPRASF_COMPRAS_PLAZOSF_COMPRAS_PUNTUALESF_PAGOS_COMPLETOSF_SALDOLÍMITE_CREDITOMESES_CLIENTEPAGOSPAGOS_MINIMOSP_AVANCE_EFECTIVOP_COMPRASSALDO
AVANCE_EFECTIVO1.0000.6060.791-0.7690.3550.822-0.428-0.679-0.6420.652-0.3920.507-0.8330.7900.5040.4240.673
COMPRAS0.6061.0000.563-0.5870.0710.660-0.278-0.833-0.0800.7030.0520.379-0.6510.4670.7230.2590.147
COMPRAS_PLAZOS0.7910.5631.000-0.8590.6110.719-0.699-0.565-0.6770.764-0.2300.762-0.7270.6800.3650.6860.568
COMPRAS_PUNTUALES-0.769-0.587-0.8591.000-0.595-0.7040.6880.5750.628-0.7680.188-0.7540.712-0.653-0.379-0.674-0.518
F_AVANCE_EFECTIVO0.3550.0710.611-0.5951.0000.200-0.7990.004-0.5770.492-0.0060.795-0.2120.246-0.1640.7980.281
F_COMPRAS0.8220.6600.719-0.7040.2001.000-0.290-0.749-0.5440.609-0.4000.376-0.8500.7890.6030.2830.642
F_COMPRAS_PLAZOS-0.428-0.278-0.6990.688-0.799-0.2901.0000.1700.500-0.654-0.101-0.8810.296-0.2690.007-0.854-0.209
F_COMPRAS_PUNTUALES-0.679-0.833-0.5650.5750.004-0.7490.1701.0000.173-0.6370.127-0.2720.745-0.590-0.748-0.150-0.306
F_PAGOS_COMPLETOS-0.642-0.080-0.6770.628-0.577-0.5440.5000.1731.000-0.3600.533-0.5160.567-0.673-0.016-0.510-0.795
F_SALDO0.6520.7030.764-0.7680.4920.609-0.654-0.637-0.3601.0000.0510.728-0.6050.4780.4600.6370.247
LÍMITE_CREDITO-0.3920.052-0.2300.188-0.006-0.400-0.1010.1270.5330.0511.0000.0870.416-0.528-0.0750.091-0.669
MESES_CLIENTE0.5070.3790.762-0.7540.7950.376-0.881-0.272-0.5160.7280.0871.000-0.3830.3360.0820.8680.245
PAGOS-0.833-0.651-0.7270.712-0.212-0.8500.2960.7450.567-0.6050.416-0.3831.000-0.805-0.596-0.288-0.663
PAGOS_MINIMOS0.7900.4670.680-0.6530.2460.789-0.269-0.590-0.6730.478-0.5280.336-0.8051.0000.4460.2690.761
P_AVANCE_EFECTIVO0.5040.7230.365-0.379-0.1640.6030.007-0.748-0.0160.460-0.0750.082-0.5960.4461.000-0.0200.182
P_COMPRAS0.4240.2590.686-0.6740.7980.283-0.854-0.150-0.5100.6370.0910.868-0.2880.269-0.0201.0000.218
SALDO0.6730.1470.568-0.5180.2810.642-0.209-0.306-0.7950.247-0.6690.245-0.6630.7610.1820.2181.000

Missing values

2025-03-12T22:41:37.817234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T22:41:38.025432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T22:41:38.163833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SALDOF_SALDOCOMPRASCOMPRAS_PUNTUALESCOMPRAS_PLAZOSAVANCE_EFECTIVOF_COMPRASF_COMPRAS_PUNTUALESF_COMPRAS_PLAZOSF_AVANCE_EFECTIVOP_AVANCE_EFECTIVOP_COMPRASLÍMITE_CREDITOPAGOSPAGOS_MINIMOSF_PAGOS_COMPLETOSMESES_CLIENTE
ID
C100015323.1488830.25069326344.0722038237.4425203727.11316215708.2396800.4965360.4644420.8216110.1945021007716819.4800421337.02746039452.958120.8179077
C1000212726.6381200.79130737958.519025690.74244018733.81096038284.3544300.6994570.2503270.6548631.0839027815615617.570588000.18362463013.748480.3431199
C100034305.5720680.17653128392.9533436009.4700902873.38323214294.1850300.4197640.5236620.8999120.207049728115515.5862127111.360490NaN0.8290746
C100044740.9885110.17807627399.0038438246.8634903402.8533756936.8125180.4396660.6065970.7831290.228299788312926.5879723919.11340038444.219980.8839847
C1000513820.9206400.82691442214.021637341.00782119273.07099040091.3478500.8214120.2835790.5013611.1063508818214404.705076994.68847462041.617340.38318610
C1000612439.0648900.70667340674.226126156.20883119401.47561032438.9127000.8367640.3439360.5278130.97069310321614554.911558814.23986558227.564690.39837710
C100075751.8751160.20263532509.1591634929.9921102951.50116315831.7776000.4337350.5721190.8487480.257982809916427.9972126477.35779019768.613170.9192826
C1000811958.8504300.73127440494.415819571.36714318972.54900034492.9710300.8712110.0684370.5091610.9689667219714040.665215679.38166952698.251580.37264910
C1000912887.0809900.87173237655.948507249.84719020221.26317038881.6783400.8849320.3263940.5622111.0114748821714514.689788457.49328857985.421200.30766510
C100104175.2392500.23452427697.4644234065.7420504428.63837114519.2191300.4455820.6246150.9208260.268456788814888.1315427044.16156030451.403820.8813086
SALDOF_SALDOCOMPRASCOMPRAS_PUNTUALESCOMPRAS_PLAZOSAVANCE_EFECTIVOF_COMPRASF_COMPRAS_PUNTUALESF_COMPRAS_PLAZOSF_AVANCE_EFECTIVOP_AVANCE_EFECTIVOP_COMPRASLÍMITE_CREDITOPAGOSPAGOS_MINIMOSF_PAGOS_COMPLETOSMESES_CLIENTE
ID
C1894116092.2897200.2118748585.33824629785.21844010502.26311017707.3457300.5527230.6952640.6938131.016379491395320.34398923574.22460047530.663780.1709168
C1894213434.2744700.75864842511.2092203679.37621718827.42953035136.7796400.7522400.2563600.5255041.0997238720213975.0780007020.07313058548.044460.41775010
C189435123.8772380.17527032472.72990032842.8876803872.89258211315.3860400.4246720.4823750.8712420.1868187810616501.83949024960.862320NaN0.8808807
C1894411658.2613300.78787740468.6237905411.36187518894.47755040461.0539000.9704390.1833220.5009011.1059219614514508.1423804600.56882556319.075070.3422709
C1894515119.9287600.24610810546.66941032155.78472011235.84688021376.1959800.5476210.7836790.5921710.965899291204865.17363919403.20505053664.116610.2086918
C189466095.2211560.23434627094.00782035917.2043803463.82137611711.5788900.4698320.5394770.8283210.1910307510117623.81028023954.39695034841.829890.8668567
C1894712682.5758200.68602538433.6188505293.54195618924.79980034444.8673200.7103310.2764260.5460590.9351529517012433.8149307375.62532266726.372990.30660010
C1894816464.3114100.1891364296.19638432554.75964011231.50043015214.6715600.4246650.7460100.6903801.063788511513307.58711722947.75922054761.962210.1292428
C1894915531.8837800.11711812219.32565031795.97170012390.43213013856.3932100.4506700.8051400.6087371.058548611786148.23291021369.50352050368.309060.0874488
C189505852.4885660.16682032480.93620032368.3577302825.5555188540.9309240.4487530.5544580.8639720.202943879117179.14746024703.92521033166.542560.8199867